Book Image

Cloud Scale Analytics with Azure Data Services

By : Patrik Borosch
Book Image

Cloud Scale Analytics with Azure Data Services

By: Patrik Borosch

Overview of this book

Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.
Table of Contents (20 chapters)
1
Section 1: Data Warehousing and Considerations Regarding Cloud Computing
4
Section 2: The Storage Layer
7
Section 3: Cloud-Scale Data Integration and Data Transformation
14
Section 4: Data Presentation, Dashboarding, and Distribution

Chapter 6: Using Synapse Spark Pools

In your modern data warehouse project, you may use Azure Data Factory ETL pipelines (see Chapter 5, Integrating Data into Your Modern Data Warehouse) to integrate and transform incoming data according to your needs. However, chances are that you are a more code-oriented developer, that you are already very proficient with Spark, or that your transformational needs reach beyond the functionality or the available compute power of Data Factory.

Maybe you need to train and implement machine learning models as part of your project, and you want a Spark engine that can scale to your needs and offers suitable libraries and tight integration with all the other tools that you plan to use on Azure.

This chapter will discuss Synapse Spark pools and how to implement them on Azure. You will learn about their architecture and how jobs are handled when they are dispatched to a cluster. You will examine how to implement notebooks and Spark jobs and integrate...